Insights From Insurance for Fair Machine Learning

TL;DR

This paper explores how insurance concepts can inform fair machine learning by examining the roles of uncertainty, fairness, and responsibility, highlighting overlooked themes like responsibility and the tension between aggregate and individual fairness.

Contribution

It introduces an interdisciplinary perspective by linking insurance fairness concepts to machine learning, emphasizing responsibility and the tension between aggregate and individual fairness.

Findings

Insurance provides valuable insights into fairness, responsibility, and uncertainty in machine learning.

The paper highlights the importance of considering responsibility and the tension between aggregate and individual fairness.

It problematizes fairness as calibration in the context of insurance-inspired perspectives.

Abstract

We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront two themes that have been largely overlooked in the machine learning literature: responsibility and aggregate-individual tensions.